In the field of electronic communication systems, requests for network services can often be duplicative or redundant, imposing network service costs for responding to the duplicative requests. For example, artificially intelligent computer systems (such as the IBM Watson™ cognitive question answering (QA) system or and other natural language question answering systems) are capable of processing questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. In operation, users submit one or more questions through a front-end application user interface (UI) or application programming interface (API) to the QA system where the questions are processed to generate answers that are returned to the user(s). When a large number of users are simultaneously submitting questions, such as during distress situations, the submitted questions are typically treated independently from one another by the QA system which generates separate procedural responses from the ingested corpus. As a result, traditional QA systems will sequentially process submitted questions in chronological order without regard to any contextual understanding of other, potentially relevant questions and/or answers. Multicast networking allows a single service to respond to multiple machines at once by relying on the networking infrastructure to distribute the responses to the appropriate clients, but such reliance on the networking infrastructure makes this solution difficult to configure and maintain in all but the most controlled environments. TCP multiplexing can provide similar efficiencies in responding to requests, but only for a single client connecting to multiple services on an individual server, and even then only with specialized client and server software to perform the multiplexing. As a result, the existing solutions for efficiently processing questions and answers are extremely difficult at a practical level.